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Data mining
We are living in the data mining age. Provide an example on how data mining can turn a large collection of data into knowledge that can help meet a current global challenge in order to improve healthcare outcomes.
APA style. 250 words minimum. One reference.

IMAGE & SIGNAL PROCESSING

Schizophrenia Auxiliary Diagnosis System Based on Data
Mining Technology

Xiaohong Wang1 & Na Zhao1 & Peng Ouyang2 & Jiayi Lin3 & Jian Hu1

Received: 26 December 2018 /Accepted: 13 February 2019
# Springer Science+Business Media, LLC, part of Springer Nature 2019

Abstract
In order to use digital medical technology to develop and design an auxiliary diagnosis system for schizophrenia to assist doctors
at all levels to diagnose and predict the cure of patients, improve the accuracy of diagnosis of symptoms, find complications in
advance, and reduce the risk of disease, the application of Bayesian network in auxiliary diagnosis system of schizophrenia is
studied, and an auxiliary diagnosis system of schizophrenia is designed. Based on data mining technology, knowledge informa-
tion can be found from patient data and used to diagnose the nature of patients. The demand analysis of auxiliary diagnosis
system is briefly introduced, and an auxiliary diagnosis system for schizophrenia based on Bayesian network is designed.

Keywords Bayesian network . Auxiliary diagnosis system . Demand analysis . Functional design . Data mining

Introduction

Schizophrenia is a serious mental disease, and the incidence is
unexplained. Emotion, perception, thinking and behavior and
other aspects of the disorder and mental activities and other
symptoms are often related to syndrome in clinical [1]. The
incidence rate is 0.007% – 0.014%, often having onset in
young adults, and the clinical cure rate is low, which brings
a lot of burden to the patients and their families [2]. In life, the
general awareness of patients with schizophrenia are relatively
clear and intelligence is basically normal, but in the disease
process, the course generally has a long period of time, and the
disease will deteriorate fast. As a result, the cognitive function
of patients is damaged, and it will cause mental decline or
mental disability [3]. But some patients, after a scientific and

reasonable treatment, can achieve rehabilitation or basic
rehabilitation.

If digital medical technology can be used to develop a
schizophrenia auxiliary diagnosis system to assist doctors
at all levels to diagnose and predict schizophrenia pa-
tients, the accuracy of diagnosis can be improved, symp-
toms can be detected in advance and the risk of onset can
be reduced [4]. Digital intelligent medical technology is
the trend of the development of medical information tech-
nology. It solves the Binformation island^ phenomenon
among different medical institutions and realizes the shar-
ing of medical information among different institutions
[5]. With the continuous development of the scale of hos-
pital clinics, the number of patients in hospital clinics is
also increasing, the number of patrols per unit time is
increasing sharply, the patient information management
is also very complex and diversified, and the traditional
way is gradually difficult to meet the requirements of
patients on the level of service, seriously affecting the
operational efficiency of medical institutions and hinder-
ing the development of medical institutions [6].
Therefore, it is necessary to build a mobile patrol infor-
mation management and query platform for schizo-
phrenics. Wireless technology is an important method
and means to update the information and data of schizo-
phrenia patients to the central system quickly and effec-
tively, to solve various problems in the mobile patrol of
schizophrenia patients for medical institutions, and to

This article is part of the Topical Collection on Image & Signal
Processing

* Jian Hu
[emailprotected]

1 Department of Psychiatry, The First Affiliated Hospital of Harbin
Medical University, 23 Youzheng Street, Nangang District,
Harbin 150001, China

2 School of Management, Harbin Institute of Technology,
Harbin 150001, China

3 Beijing Electro-Mechanical Engineering Institute, Beijing 100074,
China

Journal of Medical Systems (2019) 43:125
https://doi.org/10.1007/s10916-019-1214-8

http://crossmark.crossref.org/dialog/?doi=10.1007/s10916-019-1214-8&domain=pdf

mailto:[emailprotected]

improve the service level and management efficiency of
departments [7].

The research content of schizophrenia auxiliary diag-
nosis system is to integrate and analyse individual genetic
background data, health data, disease-related molecular
biology data and drug clinical trial data, and to form a
network electronic health system technology and data
analysis system to study disease prediction, diagnosis,
treatment, and prevention digital medical knowledge anal-
ysis method and its integrated software [8]. From a bio-
logical and medical point of view, it is difficult for biol-
ogists to discover the effects of a single or several genes
on organisms and the relationship between them as a
whole by manipulating them. However, with the develop-
ment of technology, it is now possible to analyse personal
health indicators, medical records, drug reactions and oth-
er data [9]. At the same time, genetic information, protein
family tree information, genome-wide expression and
methylation information, as well as epigenetic information
can also be analysed. If biologically multi-dimensional
and multi-directional data can be fused organically, a pa-
tient can be described completely, thus achieving precise
medical purposes for schizophrenics [10].

Method

Demand analysis of auxiliary diagnosis system
for schizophrenia

Data requirement analysis based on Bayesian algo-
rithms: According to the data characteristic information
required, case report forms are designed and subjects
are selected in the research hospital centers. Finally,
316 schizophrenic patients are selected as data to verify
the model of auxiliary diagnosis system. Among them,
the selected patient data contains 237 dimension attri-
butes and 8 of 237 dimension attributes are category
attributes. 17 attributes have more vacancy values, va-
cancy rate is about 13%, 79 attributes have discrete data
values, and other attributes are continuous data values
[11]. In the latter study, the selected schizophrenic pa-
tients sample data will be used for learning and training
to obtain decision rules, and to explore whether the data
demand characteristics can better meet the needs of the
system, and then be used for clinical auxiliary
diagnosis.

316 patients with high-dimensional small sample data
are selected for two main purposes: to study the impact of
the attributes of clinical samples on their categories, and
to explore whether the patient data meet the needs of the
auxiliary diagnosis model to guide the diagnosis process;
to find a method of mining high-dimensional small

sample data. The main reason for the analysis of high-
dimensional small sample data is that in some cases or
in a short time only some data can be obtained, and
knowledge can be obtained from these data, so it is nec-
essary to study high-dimensional small sample data [12].
The most important thing here is how to ensure and im-
prove the accuracy of the results of this auxiliary diagno-
sis model.

By mining and analyzing the small sample data in clin-
ical diagnosis, we can no longer be limited by the number
of samples; the important condition attribute set obtained
can help doctors check only a few important items when
examining patients, which can not only reduce the diag-
nosis cost of patients, but also optimize the allocation of
medical resources. The results obtained after mining and
analysis can be applied to the auxiliary diagnosis system,
and then help or assist doctors to diagnose schizophrenia
of patients.

System function business process requirement analysis:
The main purpose of data pre-processing is to process the
data with redundancy, incompleteness, noise and high di-
mensionality that cannot directly use Bayesian network,
to provide simple, clean, accurate and normal data for the
auxiliary diagnosis system of schizophrenia, and to im-
prove the efficiency and accuracy of the auxiliary diagno-
sis system information processing of schizophrenia.

Therefore, the flow chart of data pre-processing that
the auxiliary diagnosis system should adopt is shown in
Fig. 1.

The auxiliary diagnosis system of schizophrenia based
on Bayesian network is composed of Bayesian network
structure and parameters. The Bayesian network can be
used to obtain the Bayesian network structure from the
patient sample data set through structural learning, then
to learn the parameters, and finally to obtain the parame-
ters of the Bayesian network. The construction process of
Bayesian network is basically the same, and its workflow
is shown in Fig. 2.

In fact, the process of auxiliary diagnosis of schizo-
phrenia by Bayesian network is to use the Bayesian net-
work has been built to calculate and analyse the newly
added patients data, and to judge the type of the input
patients data. As a result, reasoning diagnosis is actually
a problem of classification. The process of diagnosis
should first preprocess the original information of pa-
tients, standardize the patient records, and then calculate
the patient records using Bayesian network to get the di-
agnosis results. The diagnostic workflow is shown in
Fig. 3.

The update of the sample database mainly refers to adding
new patient data information to the sample database. The pro-
cess of updating is to manage the pre-processing of the patient
information that has been diagnosed and input it into the

125 Page 2 of 7 J Med Syst (2019) 43:125

sample database to obtain a new sample database. The
workflow is shown in Fig. 4.

Demand analysis of auxiliary diagnosis function model
based on Bayesian network. Naive Bayesian is an important
branch of Bayesian decision theory. Naive Bayesian hypoth-
esis requires that the value of an attribute affects a given class
independently of other attribute values and it is a supervised
learning method. Although this harsh restriction is often not
met in reality, naive Bayesian reasoning usually implements
attribute selection process first in data sets, which improves
the independence of attributes. Moreover, naive Bayesian rea-
soning can generate more complex non-linear decision-mak-
ing surfaces, and can fit fairly complex surfaces and achieve
great success.

Based on the improved naive Bayesian method of attribute
weighting, the predictive formula of the patients untreated
probability can be obtained according to the formula as fol-
lows:

P C2=Xj

P Xj=C2

P C2
P Xj=C1

P Xj=C2

P C2
1

This involves the estimation of the class conditional prob-
ability density. P(Xk|Cj) can be obtained from the training set
by fitting the class conditional probability density (that is, the
probability density function of Xk) of the characteristic attri-
bute component Xk in each grouping Cj. According to the
value type of attribute variable Xk, the estimation methods
of class conditional probability density are different.

When XK is a discrete numerical value, then:

P Kk=Cj

Njk
Ni

2
When it is a continuous numerical value, according to the

improved naive Bayesian model method mentioned above,

that is, the conditional probability density function fitting Xk
by the kernel density estimation method according to formula
Xk, P(Xk|Cj) is calculated as follows:

P Xk=Cj

1
nh

n

t1
K

XkXt
h

3

K(x) is called the kernel function, h is called the window
width of the kernel function, that is, if the larger h is chosen,
the deviation may be larger, and the estimated probability
density function will be smoother; if smaller, the estimated
probability density function will not be so smoother, but the
probability density curve and sample fitting will be relatively
better.

The Logistic regression method and the naive Bayesian
method before and after improvement are used to establish a
model to predict the cure probability (PHM) of schizophrenia
patients in the course of treatment. The model is applied to the
auxiliary diagnosis system. The resolution performance of the
three models on validating the data set of schizophrenia pa-
tients is shown in Fig. 5.

Among them, the area under ROC (Receiver Operating
Characteristic) curve of Logistic regression model is AUC
(Area under concentration-time curve) = 0.5142 0.1095,
standard naive Bayesian model is AUC = 0.5899 0.1063,
and improved naive Bayesian model is AUC = 0.7721
0.0865. The difference has statistical significance
(P < 0.0001), which shows that the improved naive Bayesian model can better distinguish between the cured schizophrenics in the treatment process. Namely, the performance of the im- proved Naive Bayesian model applied in the auxiliary diag- nosis system is better than that of the other two models. The aim of this study is to design an auxiliary diagno- sis system for schizophrenia based on Bayesian network. Firstly, the data requirement of the auxiliary diagnosis Patient outpatient record Patient test record Data integration Feature selection Discretization Feature reduction
Missing value

processing
Sample data set

The doctor advice

Fig. 1 Data pre-processing workflow diagram

Sample data set Structure learning Bayesian structure Parameter learning
Bayesian network

parameters

Sample data set

Fig. 2 Work flow chart of Bayesian network construction

J Med Syst (2019) 43:125 Page 3 of 7 125

system is elaborated in detail, and the business process
requirement of the auxiliary diagnosis system is discussed
based on naive Bayesian model and functional method. At
the same time, the functional requirements of the auxiliary
diagnosis system in the application of the model and the
relationship between the processes in each stage are
discussed and analysed. Moreover, case data are selected
to verify and analyse the model system, so as to further
deepen the design and research of the reasoning and di-
agnosis function of the subsequent diagnosis system.
Finally, some other requirements constraints of the auxil-
iary diagnostic system are simply supplemented and
explained.

Overall design of auxiliary diagnosis system
for schizophrenia

Based on the consideration of doctors at all levels, the soft-
ware architecture design of schizophrenia auxiliary diagnosis
system can truly reflect and meet users needs for software,
thus improving the software requirements and quality of soft-
ware design. It is a bridge between software requirements and
software design.

Considering that the overall framework design of the
auxiliary diagnosis system for schizophrenia is an impor-
tant process, synthesizing the design principles of the aux-
iliary diagnosis system, as shown in Fig. 6, the overall
functional structure design of the system is that the client
of the doctor receives the pathological parameter detection
data, and the patient self-test data, doctor measurement
data, etc. collected from the detection instrument through
the network. After the initial treatment by the client, the
information is displayed on the display screen, and the

data is transmitted to the hospital group server through
the network. Combined with schizophrenia knowledge in
the knowledge base and the comparison of expert cases,
the doctor client finally gives diagnosis and treatment
suggestions.

All sample data are transmitted by network in this system,
and real-time remote diagnosis can be carried out among net-
work users at the same time.

Results and discussion

Design of data pre-processing

According to the previous research and the characteristics
of the original data of schizophrenia patients, as well as
the goals of data pre-processing in various formats of
users or patients, and the process requirements of data
pre-processing, the data pre-processing system needs to
meet the needs of data integration, feature screening, data
discretization, feature reduction and missing value pro-
cessing and other data management functions. That is to
say, the data pre-processing function can be further divid-
ed into different sub-functional modules as shown in
Fig. 7.

Data integration function is mainly to integrate the fea-
ture information of the same patient from different data
sources into a record; the feature screening function is to
remove the redundant feature information according to the
opinions of doctors and experts, and preliminarily screen
the valuable features; discretization is to discretize the
continuous feature values. Feature reduction is to use the
algorithm to reduce the dimension of the data, to further

Patient information Data pre-processing Patient records Reasoning diagnosis Diagnosis

Bayesian network

structure

Bayesian network

parameters

Fig. 3 Auxiliary diagnosis workflow

Patient outpatient record

Patient test record

Data pre-processing Update the sample data set Sample data set

Fig. 4 Workflow of updating sample base

125 Page 4 of 7 J Med Syst (2019) 43:125

process and screen out valuable feature information, and
to deal with missing feature value information.

Data integration processing mainly integrates the same
patient data in different source data formats into the same
record, and eliminates duplicate data records. According
to the characteristics of patient data, the processing func-
tions of conversion and merging should be included, as
shown in Fig. 8.

The integrated data may also have the same or similar fea-
ture values such as patients name and name, patients ID and
ID, or the features such as education level, origin, and occu-
pation, which are totally helpless for disease prediction. It is
necessary to remove these feature values so that they can
contain all of efficient information and concise data record.

According to the need of experts opinions, the similar
features predicting schizophrenia will be integrated into a
new feature, and the original features will be removed, so that
it can be more efficiently operated. For instance, family histo-
ry prediction of schizophrenia patients will be very helpful.

Design of Bayesian network construction module

In order to ensure the accuracy of the auxiliary diagnosis
system, the system adopts three different structures of
Bayesian networks, namely NB (Naive Bayesian
Network), INB (Improved Naive Bayesian network) and

AINB (the new Bayesian network model proposed).
Among them, AINB model is a new Bayesian network
model proposed here. Based on the practice of INB model
in this system, it is found that INB model cannot make
better use of medical experts experience to assist the di-
agnosis of schizophrenia. Therefore, AINB Bayesian net-
work is proposed based on INB. Therefore, the construc-
tion of Bayesian network here includes three types of
modules: NB, INB and AINB. The module construction
structure is shown in Fig. 9.

Because each type of Bayesian network consists of two
parts: structure and parameters, the construction of each
Bayesian network includes two processes: the determina-
tion of the structure of Bayesian network and the calcula-
tion of parameters of Bayesian network. The structure and
parameters of NB can be obtained directly from sample
data sets, and NB network is also the simplest type of
Bayesian network, so it is usually constructed in
reasoning.

After completing the above operations, case operation
can be carried out and current cases can be operated.
When data input is completed, the system automatically
matches similar cases according to intelligent algorithm
and calls similar diagnosis scheme in database for doc-
tors reference. At the same time, according to the input
patient information, intelligent matching estimation meth-
od gives the corresponding diagnosis results, and refers to
similar cases in the past to provide some diagnosis and
treatment programs; the visiting record module that day
shows the basic information of patients and visiting time,
case creation time and date; for online information mod-
ule, expert users can not only communicate directly with
patients or their families, but also respond to the message
questions. In the experience query module, users can
gradually diagnose patients diseases according to certain
reasoning strategies based on the knowledge stored in the
knowledge base.

In the design of the knowledge base of the system, the
expert diagnosis results of the relevant case and the sugges-
tions for the case can be given. The case management module
is to select the cases according to the standard and put them

Instrument test data

Patient self-test data

Doctor’s measurement data

Doctor user client
RS-232

Normal user client

server

Internet

Fig. 6 System overall framework diagram

Fig. 5 Resolution performance

J Med Syst (2019) 43:125 Page 5 of 7 125

into the knowledge base to form the expert case base. The
cases must be submitted through three or more doctors and
users to examine and submit together before they can be suc-
cessfully put into the expert case database. Ordinary cases can
only be queried, modified and deleted, not used as cases for
expert experience query. Exit the system after completing the
operation.

Design of feature screening module

The data pre-processing module mainly processes a series of
raw data to obtain the available sample data. Firstly, the over-
all structure of the data processing module is introduced, and
then the design of each module is further elaborated. Different
sub-functional modules can be further divided into different
data pre-processing functions.

In the collected data of schizophrenic patients, laboratory
data and outpatient data are stored in different ways. In order
to facilitate the storage of the two types of data, it is necessary
to convert the two types of data into a unified data format.

In the process of collecting raw data, the outpatient data of
a schizophrenic patient may be stored in different rows. Some
of the information in these rows may be duplicated or different
variable information may be stored in different rows. The
information in a data record is integrated into a data record,
and redundant data information is eliminated in the merging
process, which is what outpatient data row merging should do.

Because the original outpatient records and laboratory data
of schizophrenia patients are stored in different databases, in
order to get the complete data information of the same patient,
it is necessary to integrate the outpatient data records and
laboratory data records of the same patient in the same data
information record, that is, to increase the value of laboratory

data in the number of outpatient clinics and obtain the com-
plete information of the patients.

In order to improve the efficiency and accuracy of the aux-
iliary diagnosis system for schizophrenia, it is necessary to
remove duplicate, similar and useless features (columns) from
the integrated data information, and construct a feature screen-
ing module under the guidance of medical experts.

Because Bayesian networks generally require using dis-
crete values, in the research of this system, according to the
experience of experts, the standard of laboratory indicators
and the support of medical experts, the first step is to discretize
the continuous eigenvalues in the original data, which is real-
ized by program. The task of feature reduction is still to further
reduce the number of features. In the pre-processing of feature
screening, some features (attributes) that are obviously unre-
lated to the prediction of schizophrenia have been preliminar-
ily eliminated. Nevertheless, the number of remaining features
still needs to be further screened, and the attributes that are
unrelated to the prediction or have little correlation need to be
eliminated.

Feature reduction refers to the selection of a feature subset
from all features, which makes the constructed model more
excellent, also known as feature selection or attribute selec-
tion. In practical project applications, on the one hand, the
more the number of features are, the higher the existence of
irrelevant features or interdependent features will be, and with
the increase of the number of features, the performance of the
classifier will decline when it reaches a certain limit. On the
other hand, due to the limited training samples obtained, with
the increase of feature dimension, the demand of learning
algorithm for time and space will gradually increase. In some
cases, it will lead to dimension disaster, which makes the
model more complex, and thus greatly reduces the reasoning

Data pre-processing

Data integration Feature selection Discretization Feature reduction
Missing value

processing

Fig. 7 Functional structure diagram of data pre-processing module

Data integration

Format conversion of

laboratory data

Merger of outpatient

data rows

Data of outpatient

laboratory tests were

combined

Fig. 8 Function diagram of data
integration module

125 Page 6 of 7 J Med Syst (2019) 43:125

ability and application efficiency of the model. In the general
process of feature selection, an evaluation function is used to
evaluate a feature subset generated from the feature set, and
the evaluation result is compared with the stop criterion. If the
evaluation result is better than the stop criterion, it stops; oth-
erwise, the next feature subset is generated and the selection of
feature subset is continued. Generally, the validity of the se-
lected feature subset is verified.

Conclusion

The existing network technology, database technology and data
mining technology are used to construct and design an auxiliary
diagnosis system for schizophrenic patients and expert doctors
engaged in diagnosis and treatment of schizophrenia. The intro-
duction to the function and architecture of schizophrenia auxil-
iary diagnosis system is focused on, and the design of the func-
tion and database of schizophrenia auxiliary diagnosis system
based on Bayesian network is completed. The system fully and
reasonably utilizes the greatest advantages and functions of ex-
perts to help more schizophrenic patients. The system can effec-
tively reduce the misdiagnosis rate of schizophrenia and early
detect the disease and predict the development of the disease,
providing more professional services for patients.

Funding There was no dedicated funding regarding this study.

Compliance with Ethical Standards

Conflict of Interest Author Xiaohong Wang declares that he has no
conflict of interest. Author Na Zhao declares that he has no conflict of
interest. Author Peng Ouyang declares that he has no conflict of interest.
Author Jiayi Lin declares that he has no conflict of interest. Author Jian
Hu declares that he has no conflict of interest.

Ethical Approval All procedures performed in studies involving human
participants were in accordance with the ethical standards of the institu-
tional and/or national research committee and with the 1964 Helsinki
declaration and its later amendments or comparable ethical standards.

This article does not contain any studies with animals performed by
any of the authors.

Informed Consent Informed consent was obtained from all individual
participants included in the study.

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Publishers Note Springer Nature remains neutral with regard to juris-
dictional claims in published maps and institutional affiliations.

Bayesian network

construction

NB network INB network AINB network

Fig. 9 Structural diagram of
function modules for constructing
Bayesian networks

J Med Syst (2019) 43:125 Page 7 of 7 125

Journal of Medical Systems is a copyright of Springer, 2019. All Rights Reserved.

Schizophrenia Auxiliary Diagnosis System Based on Data Mining Technology
Abstract
Introduction
Method
Demand analysis of auxiliary diagnosis system for schizophrenia
Overall design of auxiliary diagnosis system for schizophrenia

Results and discussion
Design of data pre-processing
Design of Bayesian network construction module
Design of feature screening module

Conclusion
References

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